Abstract
In this research, the importance of Optical Coherence Tomography (OCT) in diagnosing and monitoring various retinal disorders, including Drusen, Diabetic Macular Edema (DME), and Choroidal Neovascularization (CNV), is highlighted. These conditions can have a significant impact on retinal health and vision. The research presents a technique that utilizes batch normalization for preprocessing OCT images. For classification of retinal disorders, the research employs the Inception v3 architecture, which is known for its effectiveness in image classification tasks. The performance of the proposed technique is evaluated using performance metrics such as sensitivity, specificity, accuracy, and precision. In this work, a total of 3,133 images were obtained from Kaggle.com. Among these, 710 images were classified as CNV, 895 as DME, 725 as drusen, and 804 as normal retinal images. Python was used for both designing and Google colab was used for executing the algorithm.
References
Evans, Jennifer R., and John G. Lawrenson. "Antioxidant vitamin and mineral supplements for slowing the progression of age‐related macular degeneration." Cochrane Database of Systematic Reviews 9 (2023).
Ruia, Surabhi, Sandeep Saxena, Chui Ming Gemmy Cheung, Jagjit S. Gilhotra, and Timothy YY Lai. "Spectral domain optical coherence tomography features and classification systems for diabetic macular edema: a review." The Asia-Pacific Journal of Ophthalmology 5, no. 5 (2016): 360-367.
Soares, Mário, Catarina Neves, Inês Pereira Marques, Isabel Pires, Christian Schwartz, Miguel Ângelo Costa, Torcato Santos, Mary Durbin, and José Cunha-Vaz. "Comparison of diabetic retinopathy classification using fluorescein angiography and optical coherence tomography angiography." British Journal of Ophthalmology 101, no. 1 (2017): 62-68.
Lee, Jessica, and Richard Rosen. "Optical coherence tomography angiography in diabetes." Current Diabetes Reports 16 (2016): 1-7.
T.R. Ganesh Babu, S. Shenbaga Devi & Rangaraj Venkatesh “Automatic deduction of glaucoma using optical coherence tomography image”, Journal of Applied Sciences, pp. 2128– 2138.2012.
Huang, David, Eric A. Swanson, Charles P. Lin, Joel S. Schuman, William G. Stinson, Warren Chang, Michael R. Hee et al. "Optical coherence tomography." science 254, no. 5035 (1991): 1178-1181.
Iejima, Daisuke, Mao Nakayama, and Takeshi Iwata. "HTRA1 Overexpression Induces the Exudative Form of Age-related Macular Degeneration." Journal of Stem Cells 10, no. 3 (2015).
L.V. Johnson, S. Ozaki, M.K. Staples, P.A. Erickson, D.H. Anderson, A potential role for immune complex pathogenesis in drusen formation, Exp. Eye Res. 70(4) (2000) 441–449
Johnson, L.V., Leitner, W.P., Staples, M.K., Anderson, D.H., 2001. Complement activation and inflammatory processes in drusen formation and age related macular degeneration. Exp. Eye Res. 73 (6), 887–896
Lu, Wei, Yan Tong, Yue Yu, Yiqiao Xing, Changzheng Chen, and Yin Shen. "Applications of artificial intelligence in ophthalmology: general overview." Journal of ophthalmology 2018 (2018).
Szegedy, Christian, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. "Rethinking the inception architecture for computer vision." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826. 2016.
Ahmed, Mumtaz, Neda Afreen, Muneeb Ahmed, Mustafa Sameer, and Jameel Ahamed. "An inception V3 approach for malware classification using machine learning and transfer learning." International Journal of Intelligent Networks 4 (2023): 11-18.
Yanagihara, Ryan T., Cecilia S. Lee, Daniel Shu Wei Ting, and Aaron Y. Lee. "Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review." Translational Vision Science & Technology 9, no. 2 (2020): 11-11.
https://towardsdatascience.com/detecting-retina-damage-from-oct-retinal-images-315b4af62938